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Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

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Doctoral School of Management and Business Administration, Szent Istvan University, 2100 Godollo, Hungary
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Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
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Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, D-09599 Freiberg, Germany
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Department of Mathematics, J. Selye University, 94501 Komarno, Slovakia
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Future Technology Research Center, College of Future, National Yunlin University of Science and Technology 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
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Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
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Faculty Laboratory of Artificial Intelligence and Decision Support (LIAAD)-INESC TEC, Campus da FEUP, Rua Roberto Frias, 4200-465 Porto, Portugal
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Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
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Authors to whom correspondence should be addressed.
Mathematics 2020, 8(10), 1799; https://doi.org/10.3390/math8101799
Received: 10 September 2020 / Revised: 11 October 2020 / Accepted: 15 October 2020 / Published: 16 October 2020
(This article belongs to the Special Issue Advances in Machine Learning Prediction Models)
This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models. View Full-Text
Keywords: data science; deep learning; economic model; ensemble; economics; cryptocurrency; machine learning; deep reinforcement learning; big data; bitcoin; time series; network science; prediction; survey; artificial intelligence; literature review data science; deep learning; economic model; ensemble; economics; cryptocurrency; machine learning; deep reinforcement learning; big data; bitcoin; time series; network science; prediction; survey; artificial intelligence; literature review
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MDPI and ACS Style

Nosratabadi, S.; Mosavi, A.; Duan, P.; Ghamisi, P.; Filip, F.; Band, S.S.; Reuter, U.; Gama, J.; Gandomi, A.H. Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods. Mathematics 2020, 8, 1799. https://doi.org/10.3390/math8101799

AMA Style

Nosratabadi S, Mosavi A, Duan P, Ghamisi P, Filip F, Band SS, Reuter U, Gama J, Gandomi AH. Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods. Mathematics. 2020; 8(10):1799. https://doi.org/10.3390/math8101799

Chicago/Turabian Style

Nosratabadi, Saeed; Mosavi, Amirhosein; Duan, Puhong; Ghamisi, Pedram; Filip, Ferdinand; Band, Shahab S.; Reuter, Uwe; Gama, Joao; Gandomi, Amir H. 2020. "Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods" Mathematics 8, no. 10: 1799. https://doi.org/10.3390/math8101799

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